Search: “papers”

Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case

arXiv:2603.20151v2 Announce Type: replace-cross Abstract: Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Detecting Stealth Sycophancy in Mental-Health Dialogue with Dynamic Emotional Signature Graphs

DGX agent

arXiv:2605.03472v1 Announce Type: new Abstract: As conversational AI therapists are increasingly used in psychological support settings, reliable offline evaluation of therapeutic response quality rem

model-releasesarxiv-cs-cl

6 May 2026

Tutorials

Direct Simultaneous Translation Activation for Large Audio-Language Models

arXiv:2509.15692v2 Announce Type: replace-cross Abstract: Simultaneous speech-to-text translation (Simul-S2TT) aims to translate speech into target text in real time, outputting translations while rec

tutorialsarxiv-cs-cl

Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution

arXiv:2605.01794v1 Announce Type: cross Abstract: Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimiz

researcharxiv-cs-ai

Disease Is a Spectral Perturbation

arXiv:2605.02949v1 Announce Type: new Abstract: We propose a novel method of understanding disease transformation from a healthy baseline with biomarker-level explainability. By modeling the biomarker

Do LLMs have core beliefs?

arXiv:2605.03255v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention

Dynamic Distillation and Gradient Consistency for Robust Long-Tailed Incremental Learning

arXiv:2605.03364v1 Announce Type: new Abstract: The task of Long-tailed Class Incremental Learning (LT-CIL) addresses the sequential learning of new classes from datasets with imbalanced class distrib

ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity

model-releasesarxiv-cs-lg

arXiv:2605.03667v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable capabilities, but their immense computational demands during training remain a critical bottleneck

Epistemic Reject Option Prediction

arXiv:2511.04855v2 Announce Type: replace Abstract: In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Rejec

researcharxiv-cs-ai

Evaluating Agentic AI in the Wild: Failure Modes, Drift Patterns, and a Production Evaluation Framework

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for controlled, single-s

Evaluating Generative Models as Interactive Emergent Representations of Human-Like Collaborative Behavior

arXiv:2605.03855v1 Announce Type: new Abstract: Human-AI collaboration requires AI agents to understand human behavior for effective coordination. While advances in foundation models show promising ca

researcharxiv-cs-ro

False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models

arXiv:2601.07885v2 Announce Type: replace-cross Abstract: Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs)

First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction

arXiv:2605.03463v1 Announce Type: new Abstract: Neural Surface Reconstruction has become a standard methodology for indoor 3D reconstruction, with Signed Distance Functions (SDFs) proving particularly

applicationsarxiv-cs-cv

FORMULA: FORmation MPC with neUral barrier Learning for safety Assurance

arXiv:2604.04409v2 Announce Type: replace Abstract: Multi-robot systems (MRS) are essential for large-scale applications such as disaster response, material transport, and warehouse logistics, yet ens

safetyarxiv-cs-ro

FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles

arXiv:2605.03337v1 Announce Type: new Abstract: The recent surge in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction. While these methods demonstrate remarkable perfor

From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model

arXiv:2605.02491v1 Announce Type: cross Abstract: Modern searches for physics beyond the Standard Model produce rapidly expanding literature containing heterogeneous information, including textual ana

From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications

arXiv:2605.02859v1 Announce Type: cross Abstract: Scientists increasingly rely on sensor-based data, yet transforming raw streams into insights across the edge-to-cloud continuum remains difficult. Pr

researcharxiv-cs-ai

From SFT to RL: Demystifying the Post-Training Pipeline for LLM-based Vulnerability Detection

arXiv:2602.14012v2 Announce Type: replace-cross Abstract: The integration of LLMs into vulnerability detection (VD) has shifted the field toward more interpretable and context-aware analysis. While po

Full-Graph vs. Mini-Batch Training: Comprehensive Analysis from a Batch Size and Fan-Out Size Perspective

arXiv:2601.22678v2 Announce Type: replace Abstract: Full-graph and mini-batch Graph Neural Network (GNN) training approaches have distinct system design demands, making it crucial to choose the approp

GRPO-TTA: Test-Time Visual Tuning for Vision-Language Models via GRPO-Driven Reinforcement Learning

arXiv:2605.03403v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has recently shown strong performance in post-training large language models and vision-language models. It ra

safetyarxiv-cs-cv

HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems

arXiv:2605.02832v1 Announce Type: new Abstract: Deciding how to distribute work between humans and AI systems is a central challenge in organisational design. Most approaches treat this as a binary ch

HeavySkill: Heavy Thinking as the Inner Skill in Agentic Harness

arXiv:2605.02396v1 Announce Type: new Abstract: Recent advances in agentic harness with orchestration frameworks that coordinate multiple agents with memory, skills, and tool use have achieved remarka

Heterogeneous Graph Importance Scoring and Clustering with Automated LLM-based Interpretation

arXiv:2605.02919v1 Announce Type: new Abstract: Urban bridge networks are critical infrastructure whose disruption can cascade into severe impacts on transportation, emergency services, and economic a

safetyarxiv-cs-lg

'I Don't Know' -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation

arXiv:2605.00957v1 Announce Type: cross Abstract: Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LL

Local Ai

Identity-Consistent Multi-Pose Generation of Contactless Fingerprints

arXiv:2605.03830v1 Announce Type: new Abstract: Contactless fingerprint recognition has gained increasing attention due to its advantages in hygiene and acquisition flexibility. However, the absence o

local-aiarxiv-cs-cv

Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM

arXiv:2605.03945v1 Announce Type: new Abstract: Standard differential privacy imposes uniform privacy constraints across all features, overlooking the inherent distinction between sensitive and insens

applicationsarxiv-cs-lg

Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects

arXiv:2511.20657v2 Announce Type: replace-cross Abstract: The development of agents with emotional intelligence is becoming increasingly vital due to their significant role in human-computer interacti

🧠 Introducing NeuralBench: a unified, open-source framework to benchmark NeuroAI models. v1.0: 36 EEG tasks, 94 datasets, task-specific + f…

model-releasesyann-lecun--x

🧠 Introducing NeuralBench: a unified, open-source framework to benchmark NeuroAI models. v1.0: 36 EEG tasks, 94 datasets, task-specific + foundation models. MEG/fMRI ready. MIT-licensed, FAIR's Brain

Joint Relational Database Generation via Graph-Conditional Diffusion Models

arXiv:2505.16527v2 Announce Type: replace Abstract: Building generative models for relational databases (RDBs) is important for many applications, such as privacy-preserving data release and augmentin

applicationsarxiv-cs-lg

KG-First, LLM-Fallback: A Hybrid Microservice for Grounded Skill Search and Explanation

arXiv:2605.01582v1 Announce Type: cross Abstract: Authoritative competency frameworks such as ESCO, ROME, and O*NET are essential for aligning education with labor market needs, yet their technical co

applicationsarxiv-cs-ai

Latent State Design for World Models under Sufficiency Constraints

arXiv:2605.01694v1 Announce Type: new Abstract: A world model matters to an agent only through the state it constructs. That state must preserve some information, discard other information, and suppor

Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic

arXiv:2601.21972v4 Announce Type: replace Abstract: Recent work has explored optimizing LLM collaboration through Multi-Agent Reinforcement Learning (MARL). However, most MARL fine-tuning approaches r

Tutorials

Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

arXiv:2605.03620v1 Announce Type: cross Abstract: Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. H

tutorialsarxiv-cs-lg

Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks

arXiv:2605.01293v1 Announce Type: new Abstract: Foundation model-driven agents often struggle with long-horizon planning due to the transient nature of purely prompting-based reasoning. While existing

LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

model-releasesarxiv-cs-lg

arXiv:2605.03328v1 Announce Type: new Abstract: Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse

LLM-Powered AI Agent Systems and Their Applications in Industry

arXiv:2505.16120v2 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered

Logic-Constrained Shortest Paths for Flight Planning

arXiv:2412.13235v4 Announce Type: replace Abstract: The logic-constrained shortest path problem (LCSPP) combines a one-to-one shortest path problem with satisfiability constraints imposed on the routi

Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

arXiv:2605.03971v1 Announce Type: new Abstract: Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors

applicationsarxiv-cs-cl

MAGE: Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory

arXiv:2605.03228v1 Announce Type: cross Abstract: As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks that

safetyarxiv-cs-cl

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach

arXiv:2511.01680v3 Announce Type: replace-cross Abstract: Social scientists are increasingly turning to unstructured datasets to unlock new empirical insights, e.g., estimating descriptive statistics

MedSR-Vision: Deep Learning Framework for Multi-Domain Medical Image Super-Resolution

arXiv:2605.03343v1 Announce Type: new Abstract: Medical image super-resolution (MedSR) is essential for improving diagnostic precision across diverse imaging modalities such as MRI, CT, X-ray, Ultraso

Memorization In Stable Diffusion Is Unexpectedly Driven by CLIP Embeddings

arXiv:2605.02908v1 Announce Type: new Abstract: Understanding how textual embeddings contribute to memorization in text-to-image diffusion models is crucial for both interpretability and safety. This

safetyarxiv-cs-cv

Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations

model-releasesarxiv-cs-lg

arXiv:2605.03511v1 Announce Type: new Abstract: Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in s

Metadata, Wavelet, and Time Aware Diffusion Models for Satellite Image Super Resolution

arXiv:2506.23566v2 Announce Type: replace Abstract: The acquisition of high-resolution satellite imagery is often constrained by the spatial and temporal limitations of satellite sensors, as well as t

Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection

arXiv:2605.02918v1 Announce Type: new Abstract: Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervi

Model Routing as a Trust Problem: Route Receipts for Adaptive AI Systems

arXiv:2605.01710v1 Announce Type: new Abstract: AI products often route requests through version aliases, service tiers, tool choices, regional endpoints, fallback rules, or safety handling before res

Hardware

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

arXiv:2603.03756v3 Announce Type: replace-cross Abstract: While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, l

hardwarearxiv-cs-cl

MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs

arXiv:2505.20740v3 Announce Type: replace Abstract: The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application